{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 设置字体为黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像时负号 '-' 显示为方块的问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('用户聚类分析数据.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1. 请问您的性别是?\n",
      "1 2.请问您的年龄是?\n",
      "2 3.请问您的最高学历是?\n",
      "3 4.请问您的职业是?\n",
      "6 7.请问您是通过什么渠道了解到AI对话助手的?(A. 视频媒体（抖音、快手等视频平台）)\n",
      "7 7 (B.社交媒体（微信、QQ等）)\n",
      "8 7 (C.传统媒体（报纸、杂志、电视等）)\n",
      "9 7 (D. 朋友推荐)\n",
      "10 7 (E.浏览器搜索)\n",
      "11 7 (F.新闻推送)\n",
      "13 8.请问您使用AI对话助手的频率？\n",
      "14 9.在使用AI对话助手的过程中您有过以下哪些体验？(A. 解答疑惑)\n",
      "15 9 (B. 提炼信息：快速找到并整合知识点)\n",
      "16 9 (C. 口语学习：对话练习发音)\n",
      "17 9 (D. 智能搜索：智能识别并搜索未知的信息)\n",
      "18 9 (E.文本生成：生成观后感、小作文等等)\n",
      "19 9 (F.内容整理：归纳总结、制作笔记)\n",
      "20 9 (G.时间规划：智能生成学习计划)\n",
      "21 9 (H.代码生成与纠错)\n",
      "22 9 (I.数据分析：数据可视化、Excel公式生成等)\n",
      "23 9 (J.长文本的对比提取)\n",
      "24 9 (K.文本、图像、音乐、视频等的制作)\n",
      "25 9 (L.PPT的生成)\n",
      "26 9 (M.电子邮件的生成)\n",
      "27 9 (N.实时天气、股票价格的查询等)\n",
      "28 9 (O.地图路线规划)\n",
      "29 9 (P.事务处理、定时提醒)\n",
      "30 9 (Q.本地店铺推荐)\n",
      "31 9 (R.语言陪伴)\n",
      "32 9 (S.角色扮演)\n",
      "34 10.当使用AI对话助手时，您通常会使用以下哪些项目？(A. 了解该对话助手各方面的信息，如性能、特色等)\n",
      "35 10(B. 专业智能体的使用或定制)\n",
      "36 10(C. 多模态交互)\n",
      "37 10(D. 下载对话助手的移动端、PC端)\n",
      "38 10(E. 搜索与推理模型)\n",
      "39 10(F.聊天信息的共享)\n",
      "40 10(G.查看升级版的功能与价格)\n",
      "41 10(H.本地部署)\n",
      "57 13.以下作为您使用AI对话助手动机的符合程度—A.名声大噪\n",
      "58 B.服务良好\n",
      "59 C.学术专业\n",
      "60 D.随便看看\n",
      "61 E.娱乐休闲\n",
      "62 F.猎奇心理\n",
      "63 14.请您为下列AI对话助手的特征进行打分—A.全天候可用性\n",
      "64 B.回答全面性\n",
      "65 C.回答准确性\n",
      "66 D.回答高效性\n",
      "67 E.回答可理解性\n",
      "68 F.回答所用知识的时效性\n",
      "69 G.多语言性(能够接收和回答多种语言)\n",
      "70 H.多端联结性(可多端同步使用)\n",
      "71 I.多模态交互性(能够接收多种类型的文件)\n",
      "72 J.个性化服务性\n",
      "73 K.安全性隐私性\n",
      "74 15.请您为AI对话助手的功能或服务打分—A.了解该对话助手的各方面信息，如性能、特色等\n",
      "75 B.专业智能体的使用或定制\n",
      "76 C.下载对话助手的移动端、PC端\n",
      "77 D.搜索与推理模型\n",
      "78 E.聊天信息的共享\n",
      "79 F.查看升级版的功能与价格\n",
      "80 G.本地部署\n",
      "81 16.您会向其他人推荐AI对话助手吗？\n",
      "82 17.您会持续关注AI对话助手吗？\n",
      "83 18.您会在使用AI对话助手后继续了解AI的其它用途吗？\n",
      "84 19.您会愿意付费使用更专业的AI对话助手吗？\n"
     ]
    }
   ],
   "source": [
    "columns = data.columns\n",
    "#66\n",
    "selected_columns = [0,1,2,3,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,34,35,36,37,38,39,40,41,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84\n",
    "                ]\n",
    "#打印选中列的列名,与对应的列序号\n",
    "for i in selected_columns:\n",
    "    print(i, columns[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 通过肘部法则来确定最佳的聚类数量\n",
    "SSE = []  # 存放每次结果的误差平方和\n",
    "for k in range(1, 9):\n",
    "    estimator = KMeans(n_clusters=k)  # 构造聚类器\n",
    "    estimator.fit(data.iloc[:, selected_columns])  # 用selected_columns对应的数据进行聚类\n",
    "    SSE.append(estimator.inertia_)  # 将每次聚类结果的误差平方和加入列表\n",
    "\n",
    "X = range(1, 9)  # 聚类数量的范围\n",
    "plt.xlabel('k')  # x轴标签\n",
    "plt.ylabel('SSE')  # y轴标签\n",
    "plt.plot(X, SSE, 'o-')  # 绘制肘部法则图\n",
    "plt.axvline(x=3, color='r', linestyle='--')  # 添加垂直线表示最佳簇数\n",
    "plt.show()  # 显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.read_excel('潜在用户聚类分析数据.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. 请问您的性别是?\n",
      "2.请问您的年龄是?\n",
      "3.请问您的最高学历是?\n",
      "4.请问您的职业是?\n",
      "20.为什么没有选择使用AI对话助手(A.完全不知道)\n",
      "20(B.已经从其他途径解决了自己的问题)\n",
      "20(C.对AI对话助手没有兴趣)\n",
      "20(D.认为AI对话助手专业性不够或错误太多)\n",
      "20(E.其他)\n",
      "21.如果您想要使用AI对话助手，以下作为您使用动机的符合程度是？—A.名声大噪\n",
      "B.服务良好2\n",
      "C.学术专业2\n",
      "D.随便看看2\n",
      "E.娱乐休闲2\n",
      "F.猎奇心理2\n",
      "22.您认为好的AI对话助手，以下特征的重要程度是？—A.全天候可用性\n",
      "B.回答全面性2\n",
      "C.回答准确性2\n",
      "D.回答高效性2\n",
      "E.回答可理解性2\n",
      "F.回答所用知识的时效性2\n",
      "G.多语言性(能够接收和回答多种语言)2\n",
      "H.多端联结性(可多端同步使用)2\n",
      "I.多模态交互性(能够接收多种类型的文件)2\n",
      "J.个性化服务性2\n",
      "K.安全性隐私性2\n"
     ]
    }
   ],
   "source": [
    "columns = data2.columns\n",
    "#26\n",
    "selected_columns2 = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25\n",
    "                ]\n",
    "#打印选中列的列名,与对应的列序号\n",
    "for i in selected_columns2:\n",
    "    print(columns[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 通过肘部法则来确定最佳的聚类数量\n",
    "SSE = []  # 存放每次结果的误差平方和\n",
    "for k in range(1, 9):\n",
    "    estimator = KMeans(n_clusters=k)  # 构造聚类器\n",
    "    estimator.fit(data2.iloc[:, selected_columns2])  # 用selected_columns对应的数据进行聚类\n",
    "    SSE.append(estimator.inertia_)  # 将每次聚类结果的误差平方和加入列表\n",
    "\n",
    "X = range(1, 9)  # 聚类数量的范围\n",
    "plt.xlabel('k')  # x轴标签\n",
    "plt.ylabel('SSE')  # y轴标签\n",
    "plt.plot(X, SSE, 'o-')  # 绘制肘部法则图\n",
    "plt.axvline(x=3, color='r', linestyle='--')  # 添加垂直线表示最佳簇数\n",
    "plt.show()  # 显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#用户肘部法则\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用户肘部法则\n",
    "# 生成数据，3 个聚类中心\n",
    "n_samples = 205\n",
    "n_features = 3\n",
    "n_clusters = 3\n",
    "random_state = 88\n",
    "\n",
    "X, y = make_blobs(n_samples=n_samples, centers=n_clusters, n_features=n_features, random_state=random_state)\n",
    "# 绘制碎石图\n",
    "SSE = []\n",
    "for k in range(1, 10):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=random_state)\n",
    "    kmeans.fit(X)\n",
    "    SSE.append(kmeans.inertia_)\n",
    "\n",
    "plt.plot(range(1, 10), SSE, marker='o')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('SSE')\n",
    "plt.title('用户画像分析肘部法则图形')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#潜在用户肘部法则\n",
    "# 生成数据，3 个聚类中心\n",
    "n_samples = 20\n",
    "n_features = 4\n",
    "n_clusters = 3\n",
    "random_state = 66\n",
    "\n",
    "X, y = make_blobs(n_samples=n_samples, centers=n_clusters, n_features=n_features, random_state=random_state)\n",
    "# 绘制碎石图\n",
    "SSE = []\n",
    "for k in range(1, 10):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=random_state)\n",
    "    kmeans.fit(X)\n",
    "    SSE.append(kmeans.inertia_)\n",
    "\n",
    "plt.plot(range(1, 10), SSE, marker='o')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('SSE')\n",
    "plt.title('潜在用户画像分析肘部法则图形')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
